System and Methods for Reducing Uplink Resources to Provide Channel Performance Feedback for Adjustment of Downlink MIMO Channel Data Rates

ABSTRACT

Systems and methods for improving the performance of a MIMO wireless communication system by reducing the amount of uplink resources that are needed to provide channel performance feedback for the adjustment of data rates on the downlink MIMO channels. In one embodiment, a method comprises encoding each of a set of data streams according to corresponding data rates, permuting the data streams on a set of MIMO channels according to a full permutation of combinations, transmitting the permuted data streams, receiving the permuted data streams, decoding and determining an SNR for each of the data streams, computing a condensed SNR metric for the set of data streams, providing the condensed metric as feedback, determining a set of individual SNR metrics for the data streams based on the condensed SNR metric, and adjusting the data rates at which the data streams are encoded based on the individual SNR metrics.

PRIORITY CLAIM Claim of Priority under 35 U.S.C. §120

The present Application for Patent is a continuation of patentapplication Ser. No. 11/508,386, filed Aug. 22, 2006, which is aContinuation-in-Part and claims priority to patent application Ser. No.11/078,470 entitled “Systems and Methods for Reducing Uplink Resourcesto Provide Channel Performance Feedback for Adjustment of Downlink MIMOChannel Data Rates” filed Mar. 11, 2005, and assigned to the assigneehereof and hereby expressly incorporated by reference herein.

BACKGROUND

1. Field

The invention relates generally to wireless communication systems, andmore particularly to systems and methods for reducing the amount offeedback that is needed to select appropriate data rates for encodingdata streams in order to maximize data throughput.

2. Background

Wireless communication systems may include multiple base stations andmultiple mobile stations. At any given time, a particular base stationmay be in communication with one or more mobile stations. Communicationsfrom the base station to the mobile stations are often referred to asthe forward link or down link, while communications from the mobilestations to the base station are referred to as the reverse link oruplink.

Data that is to be communicated between the base station and mobilestation is typically encoded, transmitted by a transmitter (either inthe base station or the mobile station,) received by a receiver (eitherin the mobile station or the base station,) and then decoded. The datais encoded at a data rate that is selected based upon the quality of thecommunication link. The better the link, the higher the data rate thatcan be used.

While the base station typically has the capacity to be able to increasethe power at which data is transmitted and thereby increase the channelquality, this may not always be desirable. For instance, if the qualityof the communication link is already sufficient to support an adequatedata rate, increasing the power may simply increase the interferencewith other communications. Base stations therefore typically implementsome sort of mechanism to control the power and data rates at which datais transmitted. This may, for example, involve measuring performance(e.g., signal-to-noise ratio, or SNR) at the mobile station, providingfeedback on the performance to the base station, and changing the datarate at which the data is encoded and transmitted based on the measuredperformance.

One of the more recent advances in wireless communications has been thedevelopment of MIMO (multiple-input, multiple-output) systems. A MIMOsystem uses multiple transmit antennas and multiple receive antennas toestablish multiple channels that can be spatially distinguished fromeach other. One of the problems that has been encountered in thedevelopment of communications using MIMO technology is the maximizationof throughput for each of the MIMO channels and the amount of feedbackthat is necessary to maximize the throughput.

One approach (referred to as Per Antenna Rate Control, or PARC) requiresthat a separate SNR value be provided as feedback for each of the MIMOchannels. This approach is not ideal because of the large amount ofuplink resources that are required to provide SNRs for each of thechannels. Another approach (referred to as Diagonal Bell LaboratoriesLayered Space Time Architecture, or D-BLAST) only requires a single SNRvalue as feedback, but requires the transmission of null signals beforetransmitting the sequence of encoded data blocks for a part of the MIMOchannels. This results in an inefficient utilization of the channels. Athird approach (referred to as Code Reuse Bell Laboratories LayeredSpace Time Architecture, or CR-BLAST) also requires only a single SNRvalue as feedback, but it uses a single common encoder to encode all theMIMO streams. As a result, it cannot take the advantage of successiveinterference cancellation (SIC) and individually optimized rate control.Unless it is incorporated with highly complex iterative demodulation anddecoding, the performance of CR-BLAST becomes much poorer than thesystems employing SIC and individually optimized rate control. It wouldtherefore be desirable to provide systems and methods in which a reducedamount of feedback (e.g., less than separate SNRs for each of thechannels) can be transmitted from the mobile station to the base stationon the uplink, in which the utilization of the channels is notdiminished by the transmission of null signals, and in which individualrate control and SIC can be applied.

SUMMARY

In one aspect of the invention, a method is disclosed which comprisesreceiving a plurality of permuted data streams over a plurality ofchannels and inversely permuting the data streams and determining aquality metric for each of the data streams and determining a condensedquality metric based on the quality metrics for each of the datastreams. The method further comprises transmitting the condensed qualitymetric to a receiver.

In another aspect of the invention, an apparatus is disclosed whichcomprises means for receiving a plurality of permuted data streams overa plurality of channels, means for inversely permuting the data streamsand means for determining a quality metric for each of the data streamsand means for determining a condensed quality metric based on thequality metrics for each of the data streams. The apparatus furthercomprises means for transmitting the condensed quality metric to areceiver.

In another aspect of the invention, a mobile station for a wirelesscommunication system is disclosed. The mobile station comprises aprocessing subsystem, a transceiver subsystem having a plurality ofreceive antennas and being coupled to the processing subsystem whereinthe processing subsystem is configured to receive permuted data streamsvia the receive antennas, inversely permute the data streams, decode thedata streams, determine an individual quality metric corresponding toeach of the data streams, determine a condensed quality metric based onthe individual quality metrics corresponding to each of the datastreams. The mobile station is further configured to control thetransceiver subsystem to transmit the condensed quality metric to a basestation.

In another aspect of the invention, an apparatus is disclosed whichcomprises a receiver for receiving a plurality of data streams over aplurality of channels, a controller for determining an individualquality metric corresponding to each of the received data streams. Theapparatus further comprises a transmitter for transmitting theindividual quality metric corresponding to a first one of the receiveddata streams and an auxiliary parameter corresponding to each remainingreceived data stream.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram illustrating the structure of anexemplary wireless transmitter;

FIG. 2 is a functional block diagram illustrating the structure of anexemplary wireless receiver;

FIG. 3 is a diagram illustrating the transmission of each of a set ofdata streams over a corresponding set of MIMO channels in accordancewith the prior art;

FIGS. 4A and 4B are a pair of diagrams illustrating the transmission ofeach of a set of data streams over every one of a set of MIMO channelsin accordance with one embodiment;

FIG. 5 is a table illustrating all of the possible permutations of fourdata streams transmitted over four MIMO channels;

FIG. 6 is a functional block diagram illustrating the structure of asystem that utilizes pseudorandom antenna permutation and successiveinterference cancellation in accordance with one embodiment; and

FIG. 7 is a flow diagram illustrating the processing and transmission ofmultiple data streams in a MIMO communication system, as well as thedetermination of a condensed metric to be provided as feedback forcontrol of the data rates in the processing of the data streams inaccordance with one embodiment.

DETAILED DESCRIPTION

One or more embodiments of the invention are described below. It shouldbe noted that these and any other embodiments described below areexemplary and are intended to be illustrative of the invention ratherthan limiting.

Embodiments of the invention which are disclosed herein address one ormore of the needs indicated above by providing systems and methods forimproving the performance of a MIMO OFDMA (Orthogonal Frequency DivisionMultiple Access) wireless communication system by reducing the amount ofuplink resources that are needed to provide channel performance feedbackfor the adjustment of data rates on the downlink MIMO channels. In oneembodiment, data streams are conventionally encoded, interleaved andmapped to modulation symbols in a base station. The modulation symbolsare then mixed according to a pseudorandom pattern and transmitted by aset of transmit antennas so that the data of each data stream istransmitted over all of the MIMO channels. In one embodiment, a fullpermutation of the possible combinations is used. The data is receivedin a mobile station, unmixed (inversely permuted) and decoded. An SNR isdetermined for each data stream. In one embodiment, the data streams aredecoded using successive interference cancellation. A condensed SNRmetric (e.g., a reference SNR and ΔSNR) is then computed and transmittedback to the base station. The base station determines SNRs for each ofthe data streams based on the condensed SNR metric and uses these SNRsto adjust the data rates at which the respective data streams areencoded. In another embodiment the data streams are decoded without SIC.In this case, ΔSNR part of the condensed SNR is set to zero.

One embodiment comprises a method including encoding each of a set ofdata streams according to corresponding data rates, mixing the datastreams on a set of MIMO channels according to a full permutation ofcombinations, transmitting the permuted data streams, receiving thepermuted data streams, inversely permuting the data streams, decodingand determining an SNR for each of the data streams, computing acondensed SNR metric for the set of data streams, providing thecondensed metric as feedback, determining a set of individual SNRmetrics for the data streams based on the condensed SNR metric, andadjusting the data rates at which the data streams are encoded based onthe individual SNR metrics.

Another embodiment comprises a MIMO wireless communication system. Thesystem includes a base station having a plurality of MIMO transmitantennas and a mobile station having a plurality of MIMO receiveantennas. The base station is configured to encode each of a pluralityof data streams according to a corresponding data rate, permute the datastreams and transmit each of the data streams over a plurality of MIMOchannels corresponding to the MIMO transmit antennas. The mobile stationis configured to inversely permute the data streams to reproduce theencoded data streams, decode the data streams and determine a qualitymetric corresponding to each of the data streams. The mobile stationthen determines a condensed quality metric based on the quality metricscorresponding to each of the data streams and transmits the condensedquality metric back to the base station. The base station is configuredto determine an individual quality metric associated with each of thedata streams based on the condensed quality metric, and then adjust thedata rates at which each of the data streams is encoded based on theindividual quality metrics.

Numerous alternative embodiments are also possible.

As described herein, various embodiments of the invention comprisesystems and methods for improving the performance of a MIMO wirelesscommunication system by reducing the amount of uplink (reverse link)resources that are needed to provide SNR/channel performance feedbackfor the adjustment of data rates on the downlink (forward link) MIMOchannels.

In one embodiment, a set of data streams in a base station are encodedusing corresponding data rates. The encoded data streams are then readyto be transmitted. Rather than transmitting each one of the encoded datastreams over a single one of the MIMO channels, however, successiveblocks in a frame of each encoded data stream are mixed and transmittedby the different ones of the MIMO channels. That is, the data streamsare permuted across the different channels.

In this embodiment, a first block of each of the data streams istransmitted by a first combination of the MIMO channels. For example, ifthere are four data streams numbered 1-4 and four MIMO channels numbered1-4, the first blocks of data streams 1-4 may be transmitted by MIMOchannels 1-4, respectively. Then, the second blocks of data streams 1-4might be transmitted by MIMO channels 2, 3, 4 and 1, respectively, andthe third blocks might be transmitted by channels 3, 4, 1 and 2,respectively. In this embodiment, the successive blocks of data streams1-4 are transmitted by each of the 24 possible permutations of MIMOchannels 1-4.

The MIMO channels transmitted by the base station are spatiallydistinguishable by the MIMO receiver of a mobile station. The mobilestation can therefore take the blocks of encoded data from each of theMIMO channels and reconstruct the encoded data streams (it is assumedthat the mobile station knows the permutation scheme used by the basestation to mix (permute) the blocks of the data streams across the MIMOchannels.) The receiver then decodes the data streams and determines anSNR for each of the data streams.

Because the blocks of each data stream have been transmitted over allfour MIMO channels, each of the four data streams will have experiencedthe same channel conditions, on the average if the channel remainsnearly static during the transmission of the whole encoded frame. As aresult, when the SNRs (averaged over a frame) are determined for each ofthe data streams, the SNR values should vary only because of theinterference cancellation that may be achieved when each data stream isdecoded and then used as feedback to remove the associated interferencefrom the remaining data streams that will be subsequently decoded. Thisis known as successive interference cancellation.

Because the SNR of the four data streams vary only as a result of thesuccessive interference cancellation, the SNR values will not varywildly, but will instead be relatively well behaved. This is true eventhough the MIMO channel conditions may be vastly different (and couldtherefore cause the SNRs of data streams transmitted separately overcorresponding single MIMO channels to vary to a much greater degree.)

The fact that the SNRs of the different data streams are relativelywell-behaved allows the SNR values to be represented, with reasonableaccuracy, in a condensed form (that is, a form which is more compactthan separately providing each of the four different SNR values.) Forexample, the SNRs can be represented by a reference SNR value and a ΔSNRvalue, where the reference SNR value corresponds to the SNR of the firstdecoded data stream, and the ΔSNR value corresponds to the differencebetween the SNR values of successive data streams.

The mobile station transmits the condensed SNR representation to thebase station via the uplink. Because the condensed SNR representation issmaller than the representation of four individual SNR values, less ofthe uplink resources are required to provide this feedback to the basestation. The base station then uses the condensed representation of theSNRs for the different data streams as the basis for adjusting the datarates with which the different data streams are subsequently encoded. Inother words, for one data stream, the base station will assume that theSNR measured by the mobile station was equal to the reference SNR valueand will adjust to the data rate for this data stream as indicated bythe reference SNR. For the next to data stream, the base station willassume a measured SNR value equal to the reference SNR value plus theΔSNR value. For the next data stream, a value equal to the reference SNRplus two times the ΔSNR value will be used, and so on, with the datarate of each data stream being adjusted accordingly.

Before discussing exemplary embodiments in detail, it will be useful todescribe the basic operation of a single physical channel in a typicalwireless communication system. Referring to FIG. 1, a functional blockdiagram illustrating the structure of an exemplary wireless transmitteris shown.

As depicted in FIG. 1, a data stream is received and processed by anencoder 110. The data stream is encoded at a selected data rate, as willbe discussed further below. The encoded data stream is forwarded to aninterleaver 120, and then to a mapper/modulator 130. The modulatedsignal is then forwarded to an antenna 140, which transmits themodulated signal.

Referring to FIG. 2, a functional block diagram illustrating thestructure of an exemplary wireless receiver is shown. In this figure,the signal transmitted by antenna 140 is received by antenna 250, and isthen forwarded to demodulator/demapper 260. The signal is demodulatedand passed to deinterleaver 270. After the signal is deinterleaved, itis decoded by decoder 280 to reproduce the original data stream. Itshould be noted that some errors may arise during the processing of thesignal by the transmitter and receiver, so “original data stream,” asused here, refers to the decoded signal, whether it is a completelyaccurate reproduction of the original signal, or contains some errors.

FIGS. 1 and 2 represent a mechanism for communicating information in asingle direction. For example, the information may be communicated froma base station to a mobile station in a cellular telephone system.Typically, communications are bidirectional, rather than unidirectional,so a similar set of structures can be used to communicate informationfrom the mobile station to the base station, as well as from the basestation to the mobile station. In this type of system, thecommunications from the base station to the mobile station are typicallyreferred to as the forward link, while the communications from themobile station to the base station are referred to as the reverse link.

As noted above, the encoding of the data stream in the transmitter isbased on a data rate that is selected for the transmission of the data.The data rate is, in turn, selected based upon the quality of thereceived signal. If the quality of the received signal is higher, ahigher data rate can be decoded by the receiver. It is thereforedesirable to increase the data rate so that higher throughput can beachieved. If the quality of the received signal is lower, only a lowerdata rate can be decoded by the receiver. In this case, it is desirableto decrease the data rate so that there are fewer errors in the decodeddata.

In order to determine the data rate that should be selected to encodethe data stream, it is first necessary to determine the quality of thereceived signal. In some systems, the quality of the signal isdetermined by measuring the signal-to-noise-ratio (SNR) of the signal.At certain SNR levels, corresponding data rates can be supported. Forexample, SNR1 can support up to data_rate1 with an acceptable errorrate, SNR2 can support up to data_rate2, and so on. These systemstherefore measure the SNR of the received signal and transmit thisinformation back to the transmitter, which then determines whether thedata rate currently being used to encode data for transmission isacceptable, too high, or too low. If the data rate is too high or toolow, a more appropriate data rate can be selected for subsequentencoding.

It is a relatively straightforward matter in this single-channelscenario to provide the SNR of the received signal as feedback for usein adjusting the data rate at which the data is encoded. The SNRinformation is sufficient for the purposes of selecting a data rate, andthis information does not constitute an especially large overhead cost.Even if the overhead cost is considered to be large, it would bedifficult to reduce this burden, as the SNR is a single value and thisinformation is necessary to determine the appropriate data rate.

Some systems, however, do not have only a single channel. For example, aMIMO (multiple input, multiple output) system has multiple physicalchannels. A MIMO transmitter has multiple antennas, each of which may beused to transmit a different one of the multiple MIMO channels.Similarly, a MIMO receiver has multiple antennas that are used todistinguish between the different physical channels transmitted by theantennas of the transmitter and to receive these separate physicalchannels.

In a typical MIMO system, each channel is processed in essentially thesame manner as a single-channel system. In other words, for eachchannel, a data stream is encoded at a selected data rate, interleaved,mapped/modulated, transmitted via a corresponding one of the MIMOantennas, received at the receiver, demapped/demodulated, deinterleavedand decoded to a construct the original data stream. This processproceeds in parallel for each of the MIMO channels.

The MIMO system is configured so that the physical channels areindependent of each other. Multiple data streams can therefore beseparately transmitted over the different channels. In other words, eachof the data streams can be transmitted by a different transmit antenna,and can be distinguished by the multiple-antenna MIMO receiver. This isillustrated in FIG. 3.

Referring to FIG. 3, a diagram illustrating the transmission of each ofa set of data streams over a corresponding set of MIMO channels inaccordance with a prior art system is shown. The system of FIG. 3 isrepresentative of, for example, a PARC system. In this system, a set ofencoded data streams 311-314 are transmitted by a set of transmitantennas 321-324. The transmitted signals are received by receiveantennas 331-334. Space-time signal processor 335 processes the receivedsignals (all of which are received by each of antennas 331-334) todistinguish data streams 341-344 (which are essentially the same as datastreams 311-314.)

Because the MIMO channels are independent of each other, the differentchannels can have different fading characteristics. In other words eachof the channels of the MIMO system could have a different SNR. As aresult, the different channels may need to encode the respective datastreams at different data rates in order to maximize the throughput ofeach of the channels.

The straightforward way to provide this SNR information would be toseparately measure the SNRs for each of the MIMO channels, and thentransmit each of these SNR values back to the transmitter, so that thedata rates for each of the channels could be selected based upon therespective measured SNR values. This is the approach used in PARCsystems. While this approach is straightforward, it requires arelatively large amount of reverse link resources. If there are n MIMOchannels, this approach requires n times more resources than thesingle-channel case. Because of the high resource cost associated withthis approach, the present systems and methods use an alternativeapproach that allows a condensed SNR metric to be returned to thetransmitter as feedback and thereby conserves reverse link resources,while allowing the selection of data rates that more nearly maximize thethroughput of the system.

Because the different MIMO channels are independent of each other, theyhave independent fading characteristics and channel quality. The SNRs ofeach of these channels are therefore also independent. Because the SNRsare independent, they may vary substantially from each other. Forexample, if there are four channels, the first channel may have an SNRof [+15] dB, the second channel may have an SNR of [−15] dB, the thirdchannel may have an SNR of 0 dB, and the fourth channel may have an SNRof [+15] dB. It is clear that, in this situation, it would be verydifficult to characterize the SNRs of all the channels in a condensedform. The present embodiments therefore employ a methodology whichensures that the SNRs will be sufficiently well-behaved to allow them tobe represented with reasonable accuracy in a condensed form.

The methodology used in the present embodiments involves thetransmission of data for each data stream over all of the MIMO channels.In other words, for each data stream, the data is processed within thetransmitter in essentially the same manner as a typical MIMO system, butrather than transmitting the data via a single one of the MIMO antennas,one block is transmitted via a first antenna, the next block istransmitted via a second antenna, and so on. The blocks of each datastream are thereby spread across all of the MIMO channels (each MIMOchannel being associated with a corresponding one of the MIMO antennas.)This is illustrated in FIGS. 4A and 4B.

Referring to FIG. 4A, a diagram illustrating the transmission of each ofa set of data streams over every one of a set of MIMO channels inaccordance with one embodiment is shown. On the right side of FIG. 4A,four data streams 411-414 are illustrated. Data streams 411-414correspond to the encoded, interleaved, mapped/modulated data that hasbeen processed by a transmitter and is ready to be transmitted over awireless link to a receiver. In particular, the multiple data streamsrepresent the data that would conventionally be transmitted over theseparate channels of the MIMO system (the antennas of the MIMOtransmitter.) Within each of the data streams, there are a series ofdata blocks. The data blocks are identified by a letter corresponding tothe data stream and a number corresponding to the position of the datablock within the data stream. The data blocks may be of any size that isconvenient for a particular implementation, but they should not be solarge that the benefit of permuting the data streams through thedifferent channels is lost.

After the data streams have undergone the conventional pre-transmissionprocessing, the blocks of each data stream are mapped to the differentantennas of the MIMO transmitter. As shown in FIG. 4A, the first set ofblocks, A1, B1, C1 and D1, are mapped to antennas 431, 432, 433 and 434,respectively. The next set of blocks, A2, B2, C2 and D2, are mapped to adifferent combination of the four antennas. Specifically, they aremapped to antennas 432, 433, 434 and 431, respectively. Put another way,the blocks of the different data streams have been rotated by one withrespect to the antennas. The third set of data blocks is rotated by oneagain, so that data blocks A3, B3, C3 and D3 are mapped to antennas 433,434, 431 and 432, respectively. Subsequent blocks are likewise mapped todifferent combinations of the antennas, to the extent possible. In oneembodiment, the series of mappings of data blocks to MIMO channelscomprises a pseudorandom pattern (as shown and described in connectionwith FIG. 5.)

Referring to FIG. 4B, a diagram illustrating the receipt of each of thetransmitted, mixed data streams at the receiver is shown. It can be seenthat each of receiver antennas 441-444 receives the combined signalstransmitted by transmitter antennas 431-434. Space-time signal processor445 processes the received signals to distinguish permuted data streams451-454. The receiver is aware of the algorithm and/or pattern for themapping of original data streams 411-414 into mixed data streams421-424. The receiver can therefore demap, or unmix, the received datablocks (451-454) to reconstruct the original data streams (461-464.)Reconstructed data streams 461-464 can then be demapped/demodulated,deinterleaved and decoded using conventional methods.

It can be seen from FIGS. 4A and 4B that the reconstructed data streamsconsist of data blocks that have been transmitted over all of the MIMOchannels, preferably in a pseudorandom pattern. For example,reconstructed data stream 411 includes data blocks A1, A2, A3. Thesedata blocks were transmitted over the first, second, third, etc. MIMOchannels. The other reconstructed data streams were likewise transmittedover all of the MIMO channels. By transmitting each data stream over allof the MIMO channels, each data stream experiences, on average, the samechannel conditions. In other words, each of the data streams hasapproximately one fourth of its data blocks transmitted over each of theMIMO channels and therefore experiences the channel conditions of eachof the MIMO channels for one quarter of the time.

Considering the example above in which the SNRs of the differentchannels varied from [+15] dB to [−15] dB, transmitting each data streamover all four of these channels would result in an average SNR ofsomewhere between [+15] dB and [−45] dB. For example, the SNR might be[+5] dB. While the SNRs of the different data streams most likely willnot be exactly the same, they should be roughly equivalent, andcertainly will be very well behaved in comparison to the SNR variationsin a typical MIMO system.

It should be noted that, in addition to providing the benefit ofequalizing the SNRs associated with the different data streams,transmitting each of the data streams over all of the MIMO physicalchannels may have additional benefits. For example, there is a benefitto using different signal paths for the transmission of a data stream,in that the diversity provides a more robust channel.

If each of the data streams is going to be transmitted over multiplephysical channels, it is necessary to determine how the different datastreams will be mixed on the channels. In other words, it is necessaryto determine which data stream will be transmitted by which antenna atany particular time. In some embodiments, it may be possible to simplyrotate the data streams through the different antennas. For example, ifthere are four channels, successive blocks of a data stream may betransmitted by antennas 1, 2, 3, 4, 1, 2, 3, 4, and so on.

While there may be benefits to using a simple rotation such as this, itis contemplated that better performance, in terms of both equalizationof the SNRs associated with the data streams and the diversity benefits,will likely be achieved if a pseudorandom pattern including a fullpermutation of the possible combinations of data streams and physicalchannels is used. A “full” permutation of combinations, as used herein,refers to all possible orders of combinations of the data streams andphysical channels. An example is shown in FIG. 5.

Referring to FIG. 5, a table illustrating all of the possiblepermutations of four data streams transmitted over four MIMO channels isshown. The data blocks corresponding to a particular data stream areidentified by the same letter. For example, all of the data blocks froma first one of the data streams are identified by the letter “A.” Thedata blocks of the second, third and fourth data streams are identifiedby the letters “B,” “C” and “D,” respectively. Each row of the tablecorresponds to a particular MIMO channel. Each column of the tablecorresponds to a successive data blocks that is transmitted on the MIMOchannel.

It can be seen that, at each point in time (i.e., in each column of thetable,) one data block is transmitted from each of the four datastreams. In the first (far left) column, data blocks from data streamsA, B, C and D are transmitted on MIMO channels 1, 2, 3 and 4,respectively. In the next column, the data streams (or MIMO channels)are rotated, so that data blocks from data streams A, B, C and D aretransmitted on MIMO channels 2, 3, 4 and 1, respectively. The datastreams are rotated to more times with the data blocks in this order.

In the fifth column, the data streams in the original order would berotated back to the original combination of data streams and MIMOchannels (i.e., data streams A, B, C and D on MIMO channels 1, 2, 3 and4, respectively.) Rather than repeating this combination, the datastreams are permuted so that data streams A, B, C and D are transmittedon MIMO channels 1, 2, 4 and 3, respectively. The data streams are thenrotated in this order until a block from each data stream has again beentransmitted on each of the MIMO channels.

This process is repeated for each permutation of the combinations ofdata streams and MIMO channels. The four data streams can be ordered insix different permutations: A-B-C-D; A-B-D-C; A-C-B-D; A-C-D-B; A-D-B-C;and A-D-C-B. Each of these orderings of the data streams can then berotated through four different MIMO channels. For example, A-B-C-D canbe transmitted on channels 1-2-3-4, 4-1-2-3, 3-4-1-2, or 2-3-4-1.Consequently, there are 24 (4 factorial, or 4!) different combinationsof the four data streams and the four MIMO channels. The transmission ofthe data streams over the MIMO channels using all of these differentcombinations is referred to file the purposes of disclosure as a fullpermutation of the combinations.

It should be noted that the system described here is intended to beillustrative, and that alternative embodiments may have differentnumbers of data streams and/or MIMO channels. For embodiments in whichthe number of data streams is equal to the number of MIMO channels, thenumber of different combinations of the data streams and MIMO channelsis given by n! (n factorial,) where n is the number of data streams/MIMO channels. Thus, for example, a system having three data streamsand three MIMO channels would have 3!, or 6, different combinations in afull permutation. A system having five data streams and five MIMOchannels would have 5!, or 120, different combinations in a fullpermutation.

Because the blocks of each of the data streams have been transmittedover all of the MIMO channels and experience essentially the samechannel conditions, the SNRs of the different data streams are wellbehaved. Ideally, the SNRs of the data streams are equivalent. It maytherefore be possible to provide feedback to the transmitter in the formof a single SNR that represents all of the data streams. This may notprovide the highest throughput for the data streams, however.

In one embodiment, the MIMO receiver is a linear receiver withoutnonlinear interference cancellation.

If there is no successive interference cancellation operation at thereceiver, the highest data rate can be achieved with only a single SNRfeedback by applying the pseudorandom antenna permutation describedabove. When the received vector of the N×N MIMO system at the symboltime k is denoted by y(k) such that

$\begin{matrix}\begin{matrix}{{y_{N \times 1}(k)} = {{{H_{N \times N}(k)}{x_{N \times 1}(k)}} + {n_{N \times 1}(k)}}} \\{= {{{x^{(1)}(k)}{h_{N \times 1}^{(1)}(k)}} + {{x^{(2)}(k)}{h_{N \times 1}^{(2)}(k)}} + \ldots + {{x^{(N)}(k)}{h_{N \times 1}^{(N)}(k)}} +}} \\{{{n_{N \times 1}(k)},}}\end{matrix} & (1)\end{matrix}$

the SNR of the i-th stream in the linear minimum mean squared error(MMSE) receiver becomes

$\begin{matrix}{{{{SNR}^{(i)}(k)} = {\frac{P}{N}{{h_{1 \times N}^{{(i)}H}(k)}\left\lbrack {N_{N \times N}^{(i)}(k)} \right\rbrack}^{- 1}{h_{N \times 1}^{(i)}(k)}}},} & (2)\end{matrix}$

where the i-th noise covariance matrix is represented by

$\begin{matrix}{{N_{N \times N}^{(i)}(k)} = {{\sigma^{2}I_{N \times N}} + {\sum\limits_{\underset{j \neq i}{j = 1}}^{N}{\frac{P}{N}{h_{N \times 1}^{(j)}(k)}{{h_{1 \times N}^{{(j)}H}(k)}.}}}}} & (3)\end{matrix}$

In (1)-(3), H_(N×N)(k)=[h_(N×1) ⁽¹⁾(k), h_(n×1) ⁽²⁾(k), . . . , h_(N×1)^((N))(k)]^(T) denotes the channel matrix, x_(N×1)(k)=[x⁽¹⁾(k), x⁽²⁾(k),. . . , x^((N))(k)]^(T) denotes the normalized signal vector, andn_(N×1)(k) denotes the background noise vector received by the N receiveantennas whose variance is σ² per dimension. Though the MIMO systemconsidered here has N data streams, N transmit antennas, and N receiveantennas, the number of MIMO transmit streams needs not be equal to thenumber of transmit antennas nor to the number of receive antennas. Thenumber of transmit antennas and that of the receive antennas need not bethe same, either.

In general, different streams will see different SNR values as there aredifferent receive channel vectors for different transmit antennas. Whenthe number of symbols in the encoding block and the system bandwidth aredenoted by K and W, the achievable data rate (bits per second) for thei-th stream of the PARC system can be calculated in a quasi-staticchannel by using the mapping (or by any other properly designed SNR-ratemapping formula) of

$\begin{matrix}{{R^{(i)} = {{\frac{W}{K}{\sum\limits_{k = 1}^{K}{\log \left( {1 + {SNR}^{(i)}} \right)}}} = {{W\; {{\log \left( {1 + {SNR}^{(i)}} \right)}.i}} = 1}}},2,\ldots \mspace{14mu},{N.}} & (4)\end{matrix}$

It should be noted that the time index k has been deliberately omittedin representing the SNR, as a quasi-static channel is assumed. These Nrequested data rates are fed back and used to encode the next N-streamdata frame. The total data rate that can be achieved by the independentstream-wise encoding is given by

$\begin{matrix}{R = {{\sum\limits_{i = 1}^{N}R^{(i)}} = {W\; {\sum\limits_{i = 1}^{N}{{\log \left( {1 + {SNR}^{(i)}} \right)}.}}}}} & (5)\end{matrix}$

Now if pseudorandom antenna permutation is applied as in FIGS. 3-4, itcan be seen that the rates of the N streams have the same value. Morespecifically, when the permuted antenna index of the i-th stream at timek is denoted by π(i, k), the achievable data rate of the i-th stream is

$\begin{matrix}{{R^{(i)} = {{\frac{W}{K}{\sum\limits_{k = 1}^{K}{\log \left( {1 + {{SNR}^{\pi {({i,k})}}(k)}} \right)}}} = {{\frac{W}{K}{\sum\limits_{k = 1}^{K/N}{\sum\limits_{j = 1}^{N}{\log \left( {1 + {SNR}^{(j)}} \right)}}}} = {\frac{W}{N}{\sum\limits_{j = 1}^{N}{\log \left( {1 + {SNR}^{(j)}} \right)}}}}}},{i = 1},2,\ldots \mspace{14mu},N,} & (6)\end{matrix}$

and all R^((i))'s have the same value. The total achievable data rate isstill given by (5) if the encoded frame size is large and a random-likecoding such as turbo coding is used. The relations between PARC andpseudorandom antenna permutation are similar, even when a linearzero-forcing (ZF) or matched-filter (MF) receiver, rather than an MMSEreceiver, are assumed. It should be noted that only antenna cyclingoperations and a single SNR feedback are needed to achieve the maximumdata rate in the linear receiver case instead of taking all thepermutations.

In one embodiment, the MIMO receiver employs a successive interferencecancellation (SIC) methodology in decoding the data streams. The SICreceiver achieves improved SNR values for some of the data streams byfirst decoding one of the data streams, then using this information tocancel some of the interference in the remaining data streams. Morespecifically, the first-decoded data stream is used to regenerate theinterference that it created during transmission. This interference canthen be canceled out of the received superposition of data streams. Asecond one of the data streams would then be decoded. Because theinterference in this data stream is reduced as a result of theinterference cancellation from the first data stream, the SNR of thesecond-decoded data stream is greater than the SNR of the first-decodeddata stream. The second-decoded data stream is then used in the samemanner as the first data stream to cancel some of the interference inthe remaining data streams. This process is repeated for each of theremaining data streams.

When this SIC methodology is used, the SNR associated with a particulardata stream corresponds to the order in which the data stream wasdecoded, with the first data stream to be decoded having the lowest SNR,and the last data stream to be decoded having the highest SNR. Becausethe SNRs of the different data streams are not the same, the datastreams can support (i.e., be encoded at) different data rates. The datastream having the lowest SNR supports the lowest data rate, while thedata stream having the highest SNR supports the highest data rate. If asingle SNR value is provided by the receiver as feedback and is used bythe transmitter as the basis for selecting a data rate to encode eachdata stream, the maximum possible throughput on the data streams havingthe higher SNRs will not be achieved. It is therefore useful in thisembodiment to provide some indication of the difference between the SNRsof the different data streams so that appropriate data rates can beselected for each of the data streams.

When an MMSE-SIC or ZF-SIC decoder is used at the receiver, the maximumdata rate cannot be achieved in a strict sense unless N SNR values areprovided as feedback. Most of the maximum data rate, however, can beachieved in a practical sense with the condensed SNR (or, reducedfeedback) by applying a proper approximation formula, as describedherein.

On the other hand, when an MF-SIC decoder incorporated with thepseudorandom antenna permutation is used, the SNR values of the otherdata streams may be more accurately calculated at the transmitter byusing the SNR of the first data stream and an average channelcorrelation factor among the streams. The instantaneous SNR of the firststream at the output of the MF (or pilot-weighted combiner) isrepresented by

$\begin{matrix}{{{SNR}^{(1)}(k)} = \frac{{h_{N \times 1}^{\pi {({1,k})}}}^{4}{P/N}}{{\sum\limits_{i = 2}^{N}{{{h_{1 \times N}^{{\pi {({1,k})}}H} \cdot h_{N \times 1}^{\pi {({i,k})}}}}^{2}{P/N}}} + {{h_{N \times 1}^{\pi {({1,k})}}}^{2}\sigma^{2}}}} & (7)\end{matrix}$

where P, N, and σ² respectively denote signal energy, number of datastreams, and the variance of the background noise. A simple way (thoughit is not optimal in terms of achievable data rate) to calculate theaverage SNR of an encoding frame is to take the ratio of the averagesignal power (or more specifically, the arithmetic mean) to the average(arithmetic mean) interference and noise power such that

$\begin{matrix}\begin{matrix}{{SNR}_{{frame},{avg}}^{(1)} \equiv \frac{\frac{1}{K}{\sum\limits_{k = 1}^{K}{{h_{N \times 1}^{\pi {({1,k})}}}^{4}{P/N}}}}{\frac{1}{K}{\sum\limits_{k = 1}^{K}\left\{ {{\sum\limits_{i = 2}^{N}{{{h_{1 \times N}^{{\pi {({1,k})}}H} \cdot h_{N \times 1}^{\pi {({i,k})}}}}^{2}{P/N}}} + {{h_{N \times 1}^{\pi {({1,k})}}}^{2}\sigma^{2}}} \right\}}}} \\{= \frac{\frac{1}{N}{\sum\limits_{j = 1}^{N}{{h_{N \times 1}^{(j)}}^{4}{P/N}}}}{\begin{matrix}{{\left( {N - 1} \right)\frac{2}{N\left( {N - 1} \right)}{\sum\limits_{i = 1}^{N}{\sum\limits_{j = {i + 1}}^{N}{{{h_{1 \times N}^{{(i)}H} \cdot h_{N \times 1}^{(j)}}}^{2}{P/N}}}}} +} \\{\frac{1}{N}{\sum\limits_{j = 1}^{N}{{h_{N \times 1}^{(j)}}^{2}\sigma^{2}}}}\end{matrix}}} \\{{\equiv \frac{\frac{P}{N\; \sigma^{2}} \cdot \frac{\sum\limits_{j = 1}^{N}{h_{N \times 1}^{(j)}}^{4}}{\sum\limits_{j = 1}^{N}{h_{N \times 1}^{(j)}}^{2}}}{{\left( {N - 1} \right) \cdot \rho_{avg} \cdot \frac{P}{N\; \sigma^{2}} \cdot \frac{\sum\limits_{j = 1}^{N}{h_{N \times 1}^{(j)}}^{4}}{\sum\limits_{j = 1}^{N}{h_{N \times 1}^{(j)}}^{2}}} + 1}},}\end{matrix} & (8)\end{matrix}$

where the average channel correlation factor is calculated by

$\begin{matrix}{{\rho_{avg} \equiv \frac{\frac{2}{N\left( {N - 1} \right)}{\sum\limits_{i = 1}^{N}{\sum\limits_{j = {i + 1}}^{N}{{h_{1 \times N}^{{(i)}H} \cdot h_{N \times 1}^{(j)}}}^{2}}}}{\frac{1}{N}{\sum\limits_{j = 1}^{N}{h_{N \times 1}^{(j)}}^{4}}}}\overset{N\sim{large}}{}{\frac{1}{N}.}} & (9)\end{matrix}$

In the same way, the average SNR of an encoding frame of the i-thstream, which is decoded after cancellation of the first i-1 streams,can be calculated. Due to the symmetric structure of the PseudorandomAntenna Permutation, a similar SNR result to that of the 1^(st) streamis achieved with a discrepancy of the effective number of interferencesignals, which is represented by

$\begin{matrix}{{SNR}_{{frame},{avg}}^{(i)} = {\frac{\frac{P}{N\; \sigma^{2}} \cdot \frac{\sum\limits_{j = 1}^{N}{h_{N \times 1}^{(j)}}^{4}}{\sum\limits_{j = 1}^{N}{h_{N \times 1}^{(j)}}^{2}}}{{\left( {N - i} \right) \cdot \rho_{avg} \cdot \frac{P}{N\; \sigma^{2}} \cdot \frac{\sum\limits_{j = 1}^{N}{h_{N \times 1}^{(j)}}^{4}}{\overset{N}{\sum\limits_{j = 1}}{h_{N \times 1}^{(j)}}^{2}}} + 1}.}} & (10)\end{matrix}$

From (8) and (10), a relation of the SNR between the 1^(st) stream andthe i-th stream can be derived so that it is

$\begin{matrix}{{{SNR}_{{frame},{avg}}^{(i)} = \frac{{SNR}_{{frame},{avg}}^{(1)}}{1 - {\left( {i - 1} \right) \cdot \rho_{avg} \cdot {SNR}_{{frame},{avg}}^{(1)}}}},} & (11)\end{matrix}$

or, equivalently, the SNR relation can be rewritten to be

$\begin{matrix}{{{SNR}_{{frame},{avg}}^{(i)} = \frac{{SNR}_{{frame},{avg}}^{(N)}}{1 + {\left( {N - i} \right) \cdot \rho_{avg} \cdot {SNR}_{{frame},{avg}}^{(N)}}}},} & (12)\end{matrix}$

through the SNR of the last stream. Therefore, if the SNR of the 1^(st)decoded stream (or the last or any other decoded stream) and the averagechannel correlation factor is available, the SNR values of the otherstreams of the Pseudorandom Antenna Permutation system incorporated withthe MF-SIC receiver can be accurately predicted. Formulae (11)-(12),however, present only one example of how the full set of SNR values ofall the data streams can be restored when only one SNR value and onecorrelation parameter are available. It should be noted that the moresophisticated effective SNR based on (6) should be provided as feedbackrather than the arithmetic-mean-based average SNR in (10) to make a morepertinent and optimized rate selection. Thus, in the actualimplementation, any other formulae which effectively account for the SNRrelations of the streams in a given MIMO system, can be used with thereference SNR and one or a series of auxiliary parameters. The auxiliaryparameter may be the average channel correlation factor, ΔSNR, or anyothers.

The SNR prediction formula in (11) or (12), which is an accuratecalculator of the SNR values in the MF-SIC receiver case, can be used asan SNR lower bound of an MMSE-SIC receiver. In fact, the SNR of the lastdecoded stream will be the same between the MF-SIC and MMSE-SIC if thebackground noise is white, and the SNR gap (i.e, MMSE SNR-MF SNR)between the other streams will highly depend on the average channelcorrelation factor. When the average channel correlation factor is small(or, most spatial signatures are nearly orthogonal one another), the gapwill be nearly zero even for the other streams (and the SNR valuesacross different streams will almost be the same); otherwise it maybecome large. Assuming that the MS returns the SNR of the last decodedstream and the average channel correlation factor of (9), the basestation can choose the rates conservatively on the basis of (12) so thatthe latter streams can be almost surely decoded once the first stream isdecoded. On the other hand, the base station can discount the reportedaverage channel correlation factor to a smaller value in considerationof the capability of the advanced receiver (i.e., MMSE-SIC): Thereported average channel correlation factor in (9) may be diminishedmore aggressively if it is large, while it is kept almost intact if itis small.

As an alternative, the mobile station can actually generate all theaverage SNR values of the N streams in the decoding stage and estimatethe optimal effective average channel correlation factor so that thecurve in (12) (or another properly designed curve for the MMSE-SIC orZF-SIC) is as close as possible to the generated SNR values. Then theSNR of the last stream and the effective average channel correlationfactor are fed back so that the base station can choose the ratesaccording to (12).

In practice, it may be possible to derive an approximate SNR relationbetter than (12) in the MMSE-SIC or ZF-SIC receiver case in terms ofsimplicity, effective description of SNR relations, etc. For example, itmay be possible to take an additive SNR relation

SNR_(frame, avg) ^((i-1))=SNR_(frame, avg) ^((i)) −f^((i))(SNR_(frame,avg) ^((N)), ρ)   (13)

or a multiplicative SNR relation

$\begin{matrix}{{SNR}_{{frame},{avg}}^{({i - 1})} = \frac{{SNR}_{{frame},{avg}}^{(i)}}{f^{(i)}\left( {{SNR}_{{frame},{avg}}^{(N)},\rho} \right)}} & (14)\end{matrix}$

for a properly selected auxiliary parameter ρ and a recursion functionf^((i))(. , .). The recursion function may take a constant value, forexample, f^((i))(SNR_(frame,avg) ^((N)), ρ)=K, for a simpleimplementation.

In one embodiment, the feedback provided by the receiver consists of areference SNR value and a ΔSNR value. Because the channel qualityexperienced by each of the data streams is essentially the same, thedifference in SNRs for each of the data streams results from thecancellation of interference when decoding successive ones of the datastreams. Because the effect of SIC on the SNR of successive data streamsis well behaved and well understood, the SNRs of the data streams can bereasonably approximated by a reference SNR value and a ΔSNR value, wherethe reference SNR value is the actual SNR for the first-decoded channel(or the last-decoded or any other pre-specified channel depending on thesystem design,) and the ΔSNR value is the improvement (or degradationdepending on the system design) in the SNR for each successively-decodedchannel. For example, the SNR of the first-decoded channel is equal tothe reference SNR, the SNR of the second-decoded channel is equal to thereference SNR plus ΔSNR, the SNR of the third-decoded channel is equalto the reference SNR plus two times ΔSNR, and so on. It should be notedthat the base station is assumed to know the order in which the mobilestation decodes the data streams and is therefore able to apply the SNRs(reference SNR plus multiple of ΔSNR) to the appropriate data streams.The calculation and the plus operation of ΔSNR can be made either in thelinear scale or in the deciBell (dB) scale. As the plus operation in thedB scale corresponds to the multiplication operation in the linearscale, the linear and the dB scaled plus operations are respectivelyequivalent to using (13) and (14) with f^((i))(SNR_(frame,avg) ^((N)),ρ)=ΔSNR(linear−scaled−value).

In an alternative embodiment, the feedback provided by the receiver mayinclude a reference SNR (i.e., a full SNR value) for one data stream orlayer and the incremental SNR values for the other layers. For example,the feedback provided by the receiver may include the full SNR value forone layer (e.g., referring to FIG. 6, “SNR1,” the SNR of the firstlayer) and also include the incremental SNR values (SNR2(dB)-SNR1(dB)),(SNR3(dB)-SNR2(dB)), and (SNR4(dB)-SNR3(dB)) for each of the remainingthree layers (i.e., the second through fourth layers). Accordingly, thetransmitter will be able to determine the actual SNR values for each ofthe layers by a full SNR value of one layer and the incremental SNRvalues of the remaining layers provided by the receiver. Thus, in theexample provided above, the transmitter can derive the full SNR valuesof the remaining layers by adding the respective incremental SNR valuesto SNR1. It will be appreciated, however, that the bitwidth to representthe SNR gaps (i.e., the incremental SNR values) is much smaller thanthat of the original, full SNR values due to permutation effects. Itwill also be appreciated that the data streams or layers need notnecessarily be limited to the four as provided in the aforementionedexample.

Referring to FIG. 6, a functional block diagram illustrating thestructure of a system that utilizes pseudorandom antenna permutation andsuccessive interference cancellation in accordance with one embodimentis shown. In this embodiment, the system consists of a transmitter 610and a receiver 620. In one embodiment, transmitter 610 is implemented ina wireless base station and receiver 620 is implemented in a wirelessmobile station to form a communication downlink. The mobile station alsoincludes a transmitter and the base station includes a receiver to forma corresponding communication uplink.

Transmitter 610 and receiver 620 are MIMO devices that are configured totransmit and receive four channels. Transmitter 610 is configured toprocess four data streams and transmit corresponding encoded datastreams over pseudorandom combinations of the four physical MIMOchannels. Receiver 620 is configured to receive the data on the fourMIMO channels, reconstruct the encoded data streams and process thisdata to regenerate the original data streams.

Referring to transmitter 610, the four original data streams arereceived by encoders 630. Each of the encoders encodes the correspondingdata stream at a data rate which is selected for that particular datastream. The encoded data symbols are then interleaved by interleavers635 and mapped to modulation symbols by mappers 640. The modulationsymbols are then mapped by permutation unit 645 to antennas 650. Themodulation symbols are then transmitted by antennas 650 according to thepermutation scheme implemented by permutation unit 645.

Referring to receiver 620, the transmitted symbols are received byantennas 655 and are forwarded to a first one of equalizers 660. Thisfirst equalizer computes the SNR for a first one of the data streams andforwards the signal to a first one of demappers 665. The encoded symbolsare then deinterleaved by first one of deinterleavers 670 and decoded byfirst one of decoders 675. The decoded data is provided to a first oneof interference cancelers 680, which regenerates the interferencecorresponding to the first data stream and cancels this interferencefrom the received signal. A similar processing path is provided forsignals corresponding to each of the remaining data streams.

After all four of the data streams have been decoded, SNRs have beendetermined for each of the data streams. As described above, the SNRs ofthe data streams are equalized by transmitting them over all of the MIMOchannels, so differences in the SNRs determined for each of the datastreams arise from the successive interference cancellations. Thereceiver can therefore compute a condensed SNR metric for thewell-behaved set of SNRs corresponding to the four data streams. In oneembodiment, this condensed metric consists of a reference SNR value anda ΔSNR value, where the ΔSNR value is the difference between SNRs ofsuccessive ones of the data streams in either linear scale or dB scale.This condensed metric is then provided as feedback to the transmitter,which can adjust the data rates at which the different data streams areencoded based on the corresponding SNRs, as determined from thecondensed SNR metric.

The operation of this system can be summarized as shown in FIG. 7. FIG.7 is a flow diagram illustrating the processing and transmission ofmultiple data streams in a MIMO communication system, as well as thedetermination of a condensed metric to be provided as feedback forcontrol of the data rates in the processing of the data streams inaccordance with one embodiment.

As shown in FIG. 7, a set of n initial data streams is first process toproduce a corresponding set of encoded data streams (700). Thisprocessing corresponds to the encoding, interleaving andmapping/modulating of a whole data frame performed by components 630,635 and 640 of transmitter 610. Successive portions (e.g., blocks) in aframe of each of the encoded data streams are then transmitted onalternate ones of a plurality of MIMO channels (705). As noted above,the transmission on alternate ones of the MIMO channels can, forexample, follow a pseudorandom pattern. In one embodiment, thepseudorandom pattern includes all of the possible permutations of thecombinations of data streams and MIMO channels. The mixing andtransmission of the encoded data streams corresponds to components 645and 650 of transmitter 610.

The transmitted data is then received by the receiver (710). Thereceiver is a MIMO receiver that can spatially distinguish the differentMIMO channels. The mixed portions of the data streams are unmixed andthe encoded data streams are reconstructed (715). After the encoded datastreams are reconstructed, an SNR is determined for each of the encodeddata streams, and the encoded data streams are decoded to the initialdata streams (720, 725). As described above, in the embodiment of FIG.6, the data streams are decoded sequentially and are used to regenerateand then cancel the interference corresponding to the decoded datastreams.

When the SNRs for each of the data streams have been determined, acondensed SNR metric is computed from these values (730). As discussedabove, the condensed metric in one embodiment comprises a reference SNRvalue and a ΔSNR value. The condensed SNR metric is then sent back tothe transmitter (735). As previously noted, transmitter 610 and receiver620 form the downlink of a wireless communication system which alsoincludes an uplink transmitter and receiver (not shown in FIG. 6) whichare used to transmit the condensed SNR metric as feedback. When thecondensed SNR metric is received, the SNRs for each of the data streamsare reconstructed (740), and the data rates at which each of the datastreams are encoded are adjusted based upon these SNR values (745). Ifthe receiver does not use successive interference cancellation, the ΔSNRis set to 0 in the linear scale case and 0 dB in the dB scale case.

In one embodiment, the receiver may additionally feed back theinformation that requests turning off some of the transmit antennas.Then, the presented pseudorandom antenna permutation and condensed SNRfeedback will be applied only to the active transmit antennas which areactually transmitting data streams.

In another embodiment, the number of active data streams (N_(s)) may besmaller than the number of transmit antennas (N_(t)). Then, N_(t)−N_(s)transmit antennas might not transmit any signal at a given time. Even inthis case, the pseudorandom antenna permutation and the condensed SNRfeedback can be applied by considering that there are N_(t)−N_(s) moredata streams, all of which have zero transmit power.

As noted above, the foregoing embodiments are illustrative of theinvention, rather than limiting. Alternative embodiments may havenumerous variations from the systems and methods described above. Forexample, alternative embodiments may use a condensed feedback metricthat comprises a value other than a reference SNR value and a ΔSNRvalue. In fact, the metric may comprise values other than SNRs, such aserror rates in the received, decoded data streams. Alternativeembodiments may also have different types of receivers (e.g., non-SIC),different numbers of channels, and other variations.

Although not discussed in detail above, it should be noted that thefunctionality described above may be implemented in mobile stations andbase stations of a wireless communication system by providing suitableprograms that are executed in the respective processing subsystems ofthese devices. The processing subsystems then control the processing ofthe data and transmission/receipt of the data by the respectivetransceiver subsystems of the mobile stations and base stations.

The program instructions are typically embodied in a storage medium thatis readable by the respective processing subsystems. Exemplary storagemedia may include RAM memory, flash memory, ROM memory, EPROM memory,EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or anyother form of storage media known in the art. Such a storage mediumembodying program instructions for implementing the functionalitydescribed above comprises an alternative embodiment of the invention.

Those of skill in the art will understand that information and signalsmay be represented using any of a variety of different technologies andtechniques. For example, data, instructions, commands, information,signals, bits, symbols, and chips that may be referenced throughout theabove description may be represented by voltages, currents,electromagnetic waves, magnetic fields or particles, optical fields orparticles, or any combination thereof.

Those of skill would further appreciate that the various illustrativelogical blocks, modules, circuits, and method steps described inconnection with the embodiments disclosed herein may be implemented aselectronic hardware, computer software, or combinations of both. Toclearly illustrate this interchangeability of hardware and software,various illustrative components, blocks, modules, circuits, and stepshave been described above generally in terms of their functionality.Whether such functionality is implemented as hardware or softwaredepends upon the particular application and design constraints imposedon the overall system. It should also be noted that the illustrativecomponents, blocks, modules, circuits, and steps may be reordered orotherwise reconfigured in alternative embodiments. Skilled artisans mayimplement the described functionality in varying ways for eachparticular application, but such implementation decisions should not beinterpreted as causing a departure from the scope of the presentinvention.

The various illustrative logical blocks, modules, and circuits describedin connection with the embodiments disclosed herein may be implementedor performed with a general purpose processor, a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), afield programmable gate array (FPGA) or other programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof designed to perform the functions described herein.A general purpose processor may be a microprocessor, but in thealternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

The previous description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the generic principles definedherein may be applied to other embodiments without departing from thespirit or scope of the invention. Thus, the present invention is notintended to be limited to the embodiments shown herein but is to beaccorded the widest scope consistent with the principles and novelfeatures disclosed herein.

What is claimed is:
 1. A method, comprising: receiving a plurality ofpermuted data streams over a plurality of channels; inversely permutingthe data streams; determining a quality metric for each of the datastreams; determining a condensed quality metric based on the qualitymetrics for each of the data streams; and transmitting the condensedquality metric to a receiver.